Technical Talk @ LinkedIn SF - Content Relevance

October 17, 2014

LinkedIn's engineering teams build state of the art systems and algorithms that connect the world's professionals to make them more productive and successful. Up and down the stack, from best of class distributed systems like Apache Kafka to cutting edge mobile frameworks and take-no-prisoners machine learning infrastructure, LinkedIn engineering solves the hardest, most rewarding problems in the industry.

We hold Technical Deep Dives to give a closer look at some of our critical infrastructure and to learn more about the best practices we've developed during more than a decade of building systems at Internet scale. Next week we are holding our first Technical Deep Dive at our new San Francisco R&D office and invite interested engineers to attend.

How LinkedIn Recommends Content
Kumar Chellapilla, Senior Manager, Content Relevance

Thursday, October 23rd
7:00 pm - Food, Drinks & Networking
7:30 pm - Technical Talk

New LinkedIn SF Office
505 Howard St.
San Francisco, 94110

LinkedIn is a rich source of information and knowledge for professionals. Users produce and consume content in a variety of ways. Users can share information, write and publish posts, follow influencers and channels, join and engage with groups, etc. During this talk, I’ll cover several interesting aspects of how we use data mining and machine learning to recommend the most relevant and useful content to our users across such a large variety of scenarios. Similar problems are solved in many data rich companies like Google, Facebook, Twitter, Amazon, and Netflix. I’ll talk about what makes some problems very easy while other problems hard because of the unique properties of LinkedIn’s content and user data.

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